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Record W4416018870 · doi:10.1016/j.joca.2025.11.001

Usefulness of 3D joint space width on weight-bearing CT in comparison with 2D joint space width on radiographs for predicting 24-month worsening of knee osteoarthritis pain and function in the MOST study

2025· article· en· W4416018870 on OpenAlexaboutno aff
Ryo Yoshikawa, Neil A. Segal, Irina Tolstykh, Michael Ho, Donald D. Anderson, J.A. Lynch, J. Duryea, Michael C. Nevitt

Bibliographic record

VenueOsteoarthritis and Cartilage · 2025
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute on AgingNational Institutes of HealthBoston UniversityUniversity of California, San FranciscoUniversity of IowaUniversity of Kansas
KeywordsOsteoarthritisRadiographyJoint (building)Knee JointSpace (punctuation)Joint pain

Abstract

fetched live from OpenAlex

Objective This study compared the predictive validity of three-dimensional joint space width (3D-JSW) on weight-bearing computed tomography (WBCT) versus two-dimensional joint space width at predetermined mediolateral locations (2D-JSW x ) on radiographs for worsening knee pain and physical function over 24 months. Design Data from 302 participants (425 knees) in the Multicenter Osteoarthritis Study (MOST) were analyzed. Baseline assessments included bilateral standing radiographs for 2D-JSW x and WBCT for 3D-JSW. Minimal clinically important worsening (MCIW) of pain (Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale) and function (20-meter walk, sit-to-stand, WOMAC function subscale) over 24 months was assessed. Logistic regression with generalized estimating equations (GEE) accounted for within-person correlation, and predictive validity was evaluated by comparing receiver operating characteristic (ROC) areas under the curves (AUC). Results Participants had a mean age of 63.2±8.9 years and body mass index (BMI) of 28.2±4.8 kg/m². Over 24 months, 38 knees exhibited worsening WOMAC pain, with no significant association for either 3D-JSW or 2D-JSW x . For WOMAC pain, 3D-JSW (AUC=0.578) was not superior to 2D-JSW x (AUC=0.511; difference: 0.067 (95% confidence interval (CI): -0.040, 0.175)). Similar results were noted for the 20-meter walk (difference: 0.003 (95% CI: -0.049, 0.055)), sit-to-stand test (difference: 0.014 (95% CI: -0.034, 0.062)), and WOMAC function (difference: -0.020 (95% CI: -0.091, 0.050)). Conclusions 3D-JSW on WBCT did not outperform 2D-JSW x on radiography for predicting knee pain and functional worsening. However, WBCT offers several advantages, including improved imaging capabilities, that may facilitate assessment of osteoarthritis in clinical practice and research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.230
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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